(Wydawnictwo Politechniki Łódzkiej, 2023) Kucharski, Przemysław; Ślot, Krzysztof
The presented paper proposes a novel approach for detecting unknown
polymorphic patterns in sequences composed of random symbols and
of known polymorphic patterns. We propose to represent rules that drive
pattern generation as regular expressions. To detect unknown patterns, we
first incorporate knowledge on known rules into a Convolutional Autoencoder
(CAE), then we train the CAE with additional objective to prevent
weights from learning the already known patterns. Analysis of training results
provides statistically significant information on presence or absence of
polymorphic patterns that were not previously known.
